As we know, science has a lot more to do with the sociology of research than we like to think (say hello to Alan Sokal). Bioinformatics has fallen short of its lofty initial goals because it became a prime example of what nefarious effects the struggle for publication can cause, and also of the alienation of a whole field of scientists by another field of...scientists (?)
The failures of Bioinformatics have to do with it becoming a gold mine for publication-hungry CS PhDs who - if you're familiar with some of the field's exploding literature in the early 00s (or "noughties" for some) - knew really close to nothing about the biology of what they were researching, and produced pile after pile of useless algorithms and "data driven discoveries" far removed from biological or clinical phenomena. A lot of these PhDs seemed to assume the medical and biological professionals had little to contribute, since they were utterly incapable of doing Math. That is how you got things like a room full of Mathematicians and Physicists discussing how to model viral activity, without a single real Biologist specialist in the room (I am not making this up).
So, the field quickly became inundated with research whose only sole purpose was carving out a name in the publication game for Physics and Computer Science majors who had failed to land a position in their original field. Naturally, real doctors and biologists looked at the sometimes infantile-minded simplifications (of Immunology, Metabolism, brain electric activity, genomic modelling, etc.) and sometimes downright asinine assumptions and just walked away. The literature become a huge pile of impenetrable research, to the delight of the graph algorithm researcher (to name one of these sub-fields), but completely alienated from bench biology. Since the clinical phenomena became an excuse for abstraction while the field exploded with new journals, I guess the real doctors and biologists continued on with their research, largely unimpressed with Bioinformatics. They could play their publication game all they liked, while doctors and biologist would continue doing their Real World research. It's a tale from the history of science that needs to be written, because it amounts to almost a decade of high hopes and lost expectations.
And all I mentioned can probably be researched, too. If you look at the papers and their impact, what do you see? How much of that is relevant for research that came later? What's the quantity of dead-end "data driven research"? I'm not thinking about the seminal algorithms, of course, but the spike of...noise that came later...
And to say nothing of the lack of real paradigmatic change in the way the Computer Science was done, with systems full of state, and the lack of theory for real concurrent biological systems. Which is nothing but a reflection of the poor state of the field of Computer Science for such systems. Likewise, the field seemed to approach things with brute force: throw more computing power at it, and metabolic networks will be solved, protein-folding will be solved. Seems like all that C++ and clock cycles weren't really cost-effective...ya think?